Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "205"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 205 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 22 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 22 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 205, Node N19:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2459998 RF_ok 100.00% 0.00% 0.00% 0.00% - - 7.555513 0.797691 3.118928 -0.748114 4.974556 -0.535768 37.199173 6.638331 0.3670 0.6126 0.4391 nan nan
2459997 RF_ok 100.00% 0.00% 0.00% 0.00% - - 8.304760 0.990997 3.302357 -0.682817 5.062371 -0.765764 63.536652 12.039441 0.3736 0.6210 0.4469 nan nan
2459996 RF_ok 100.00% 0.00% 0.00% 0.00% - - 8.956372 0.823132 4.501225 -0.599584 4.558300 -1.076451 23.776744 3.078405 0.3962 0.6308 0.4513 nan nan
2459995 RF_ok 100.00% 0.00% 0.00% 0.00% - - 9.123553 0.830031 4.454594 -1.320039 5.216897 -0.127822 19.885697 2.461988 0.3853 0.6266 0.4416 nan nan
2459994 RF_ok 100.00% 0.00% 0.00% 0.00% - - 8.930022 0.644544 3.127909 -0.788292 5.068320 -0.708706 17.742199 3.914376 0.3881 0.6226 0.4362 nan nan
2459993 RF_ok 100.00% 0.00% 0.00% 0.00% - - 9.802806 1.153156 3.194472 -1.138731 7.027695 0.129108 13.186614 4.342625 0.3533 0.6145 0.4572 nan nan
2459991 RF_ok 100.00% 0.00% 0.00% 0.00% - - 10.199209 1.212298 3.495713 -1.158539 6.096314 -0.764930 11.592993 2.940658 0.3773 0.6103 0.4510 nan nan
2459990 RF_ok 100.00% 0.00% 0.00% 0.00% - - 8.441539 1.057158 2.790063 -0.666135 4.979019 1.239872 10.506694 2.206926 0.3702 0.6115 0.4466 nan nan
2459989 RF_ok 100.00% 0.00% 0.00% 0.00% - - 9.644461 1.820930 3.415661 -0.662962 6.648746 -0.800566 1.584419 2.442858 0.2842 0.6096 0.4600 nan nan
2459988 RF_ok 100.00% 0.00% 0.00% 0.00% - - 11.460457 1.793031 3.902078 -1.335827 8.872098 -0.993119 -0.094039 2.577014 0.2988 0.6151 0.4555 nan nan
2459987 RF_ok 100.00% 0.00% 0.00% 0.00% - - 9.486414 1.184444 3.277788 -1.338652 4.956093 -0.833110 0.382339 4.626873 0.3118 0.6134 0.4535 nan nan
2459986 RF_ok 100.00% 0.00% 0.00% 0.00% - - 11.483955 1.761996 4.212554 -1.257388 7.482034 -0.892078 3.476736 1.093013 0.3597 0.6455 0.4274 nan nan
2459985 RF_ok 100.00% 0.00% 0.00% 0.00% - - 10.521771 1.464660 3.276829 -1.302352 5.378643 -0.991532 0.268374 6.685114 0.3412 0.6196 0.4510 nan nan
2459984 RF_ok 100.00% 0.00% 0.00% 0.00% - - 9.595345 1.389908 3.420845 -1.265851 6.980988 -0.901519 1.257132 4.689664 0.3875 0.6377 0.4295 nan nan
2459983 RF_ok 100.00% 0.00% 0.00% 0.00% - - 9.508304 -1.031620 3.080009 -1.117598 6.664239 -0.813929 1.304194 0.743488 0.3988 0.6615 0.4208 nan nan
2459982 RF_ok 100.00% 0.00% 0.00% 0.00% - - 7.898224 0.671356 2.787602 -0.105895 3.173006 -1.093497 0.505557 0.243257 0.4931 0.6867 0.3671 nan nan
2459981 RF_ok 100.00% 0.00% 0.00% 0.00% - - 8.851447 0.354205 3.081385 -0.930425 7.454852 -0.296023 0.227024 7.465437 0.3628 0.6123 0.4384 nan nan
2459980 RF_ok 100.00% 0.00% 0.00% 0.00% - - 9.662579 0.719061 3.711307 0.492775 7.775987 -0.896460 3.234910 1.343779 0.3553 0.6484 0.4107 nan nan
2459979 RF_ok 100.00% 0.00% 0.00% 0.00% - - 10.005409 1.015862 3.237861 0.418780 7.733412 -0.701481 -0.435489 5.395488 0.2732 0.5931 0.4504 nan nan
2459978 RF_ok 100.00% 0.00% 0.00% 0.00% - - 6.269419 1.093728 1.750156 -0.252626 4.905473 -0.596158 0.539609 8.829312 0.4619 0.5945 0.4026 nan nan
2459977 RF_ok 100.00% 0.00% 0.00% 0.00% - - 5.826848 1.157178 1.732220 -0.108405 3.705583 -0.532550 0.837810 9.512477 0.4445 0.5615 0.3573 nan nan
2459976 RF_ok 100.00% 0.00% 0.00% 0.00% - - 5.783300 1.142623 2.372505 0.437550 4.008853 -0.650742 0.503852 7.437240 0.4832 0.6010 0.3884 nan nan

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 205: 2459998

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Temporal Discontinuties 37.199173 7.555513 0.797691 3.118928 -0.748114 4.974556 -0.535768 37.199173 6.638331

Antenna 205: 2459997

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Temporal Discontinuties 63.536652 8.304760 0.990997 3.302357 -0.682817 5.062371 -0.765764 63.536652 12.039441

Antenna 205: 2459996

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Temporal Discontinuties 23.776744 8.956372 0.823132 4.501225 -0.599584 4.558300 -1.076451 23.776744 3.078405

Antenna 205: 2459995

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Temporal Discontinuties 19.885697 9.123553 0.830031 4.454594 -1.320039 5.216897 -0.127822 19.885697 2.461988

Antenna 205: 2459994

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Temporal Discontinuties 17.742199 8.930022 0.644544 3.127909 -0.788292 5.068320 -0.708706 17.742199 3.914376

Antenna 205: 2459993

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Temporal Discontinuties 13.186614 9.802806 1.153156 3.194472 -1.138731 7.027695 0.129108 13.186614 4.342625

Antenna 205: 2459991

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Temporal Discontinuties 11.592993 10.199209 1.212298 3.495713 -1.158539 6.096314 -0.764930 11.592993 2.940658

Antenna 205: 2459990

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Temporal Discontinuties 10.506694 1.057158 8.441539 -0.666135 2.790063 1.239872 4.979019 2.206926 10.506694

Antenna 205: 2459989

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Shape 9.644461 1.820930 9.644461 -0.662962 3.415661 -0.800566 6.648746 2.442858 1.584419

Antenna 205: 2459988

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Shape 11.460457 1.793031 11.460457 -1.335827 3.902078 -0.993119 8.872098 2.577014 -0.094039

Antenna 205: 2459987

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Shape 9.486414 9.486414 1.184444 3.277788 -1.338652 4.956093 -0.833110 0.382339 4.626873

Antenna 205: 2459986

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Shape 11.483955 1.761996 11.483955 -1.257388 4.212554 -0.892078 7.482034 1.093013 3.476736

Antenna 205: 2459985

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Shape 10.521771 1.464660 10.521771 -1.302352 3.276829 -0.991532 5.378643 6.685114 0.268374

Antenna 205: 2459984

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Shape 9.595345 9.595345 1.389908 3.420845 -1.265851 6.980988 -0.901519 1.257132 4.689664

Antenna 205: 2459983

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Shape 9.508304 9.508304 -1.031620 3.080009 -1.117598 6.664239 -0.813929 1.304194 0.743488

Antenna 205: 2459982

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Shape 7.898224 7.898224 0.671356 2.787602 -0.105895 3.173006 -1.093497 0.505557 0.243257

Antenna 205: 2459981

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Shape 8.851447 0.354205 8.851447 -0.930425 3.081385 -0.296023 7.454852 7.465437 0.227024

Antenna 205: 2459980

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Shape 9.662579 0.719061 9.662579 0.492775 3.711307 -0.896460 7.775987 1.343779 3.234910

Antenna 205: 2459979

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
205 N19 RF_ok ee Shape 10.005409 10.005409 1.015862 3.237861 0.418780 7.733412 -0.701481 -0.435489 5.395488

Antenna 205: 2459978

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
205 N19 RF_ok nn Temporal Discontinuties 8.829312 1.093728 6.269419 -0.252626 1.750156 -0.596158 4.905473 8.829312 0.539609

Antenna 205: 2459977

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
205 N19 RF_ok nn Temporal Discontinuties 9.512477 5.826848 1.157178 1.732220 -0.108405 3.705583 -0.532550 0.837810 9.512477

Antenna 205: 2459976

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
205 N19 RF_ok nn Temporal Discontinuties 7.437240 1.142623 5.783300 0.437550 2.372505 -0.650742 4.008853 7.437240 0.503852
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